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A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
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Luke T. Hudson ,
Devin W. Laurence,
Chung-Hao Lee ,
Sanjay Pant
Journal of the Mechanical Behavior of Biomedical Materials, Volume: 138, Start page: 105657
Swansea University Authors:
Ankush Aggarwal , Sanjay Pant
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DOI (Published version): 10.1016/j.jmbbm.2023.105657
Abstract
A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be...
| Published in: | Journal of the Mechanical Behavior of Biomedical Materials |
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| ISSN: | 1751-6161 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62267 |
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| Abstract: |
A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May–Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May–Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee–Sacks model and the May–Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode’s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based. |
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| Keywords: |
Soft-tissue; Aortic valve; Constitutive model; Model selection; Bayesian; Biomechanics |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by grant EP/P018912/2 from the Engineering and Physical Sciences Research Council of the UK and grant R01 HL159475 from the National Institutes of Health, United States. |
| Start Page: |
105657 |